Work

Physics-Guided Temporal Underwater Image Enhancement Using Implicit Neural Representations and Diffusion Models

Year: 2026

Type: article

Abstract: Underwater image enhancement is affected by light absorption, scattering, and non-uniform illumination, typically resulting in color shifts, contrast reduction, and blurred details. Existing diffusion-based underwater image enhancement methods possess strong generative capabilities but face three major issues: lack of explicit modeling of the underwater imaging process, unstable conditional input under complex degradation, and insufficient temporal consistency in consecutive frames. To address these challenges, this work proposes a physics-guided temporal diffusion framework for underwater image enhancement, PG-TIE, which integrates physics-guided priors, implicit reconstruction, and temporal diffusion within a unified framework. Specifically, we first design a physics-guided prior generation module (PGPG) to explicitly estimate transmission maps and background light, providing interpretable guidance for subsequent restoration. Next, the implicit neural reconstruction branch (INTR) constructs stable conditional features to reduce diffusion difficulty. Finally, the temporal physics-guided diffusion Transformer branch (TPDT) incorporates physics priors, conditional features, and temporal memory to enhance both single-frame quality and consistency across consecutive frames. Experimental results on the UIEBD, LSUI, and U45 datasets demonstrate that PG-TIE achieves superior performance across reference and non-reference metrics. On UIEBD, it achieves a PSNR of 24.14 dB, SSIM of 0.8905, LPIPS of 0.1015, and FID of 24.32; on LSUI, PSNR 27.93 dB, SSIM 0.9521, LPIPS 0.1007, and FID 26.38; and on U45, URanker 2.812, MUSIQ 52.152, UCIQE 0.606, UIQM 3.176, and NIQE 3.897. Additionally, PG-TIE achieves a tLPIPS, Warping Error, and Flicker Index of 0.031, 0.284, and 4.72, respectively, validating its temporal stability in consecutive frames. Ablation studies further confirm the effectiveness of PGPG, INTR, TPDT, and their key components.

Source: Journal of Marine Science and Engineering

DOI: 10.3390/jmse14090798

Authors: Fubin Zhang, Fubin Zhang, Zichi Zhang, Feihu Zhang, Feihu Zhang, Xinbo Tian

Institution: Northwestern Polytechnical University

Language: English

Topic: Image Enhancement Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: National Key Research and Development Program of China